Additionally, we use this map to create a more general semantic feature which assigns to each predicate argument a frame element label.

the features for em 8 (acquire) are FEA0:BUYER, FEA1:GOODS, FEA3:MONEY, and FEATMP:TIME.


The iFHMM framework uses the Markov Indian buffet process (mIBP) (Van Gael et al., 2008b) in order to represent each object as a sparse subset of a potentially unbounded set of latent features (Griffiths and Ghahramani, 2006;Ghahramani et al., 2007;Van Gael et al., 2008a).

4 the mIBP defines a distribution over an unbounded set of binary Markov chains, where each chain can be associated with a binary latent feature that evolves over time according to Markov dynamics.


, f M } that is represented in the matrix by a binary vector , where F i t = 1 indicates that f i is associated with y t .

f decomposes the observations and represents them as feature factors, which can then be associated with hidden variables in an ifHMM model as depicted in figure 2(c).


For simplicity, we also use F c and D e to denote a constituency forest and a dependency tree respectively.

the size of tails(h d ) of a hyperedge h d in a dependency tree is a constant one.


The two Chinese phrases (4) and (5) cross, and therefore cannot both be constituents in the same tree.

exact tree-to-tree extraction commits to a single structural analysis but fuzzy tree-to-tree extraction pursues many restructured analyses at once.


First, Table 4 shows that the system using the tree-to-tree grammar used the glue rule much less and performed more matching substitutions.

in order to minimize errors on the tuning set, the model learned to build syntactically richer and more well-formed derivations.


We can describe this mapping via word-tophrase projections, as illustrated in Figure 1.

let word e i project to the phrasal span σ(e i ), where (1) and likewise each word f j projects to a span of e. Then, every word in one of the phrasal spans must project within the other.


Optimizing for a bispan F-measure penalizes alignment mistakes in proportion to their rule extraction consequences.

adding a word link that prevents the extraction of many correct phrasal rules, or which licenses many incorrect rules, is strongly discouraged by this loss.


Maintaining the context necessary to score non-local bispans further increases running time.

iTG inference is organized around search states associated with a grammar symbol and a bispan; augmenting grammar symbols also augments this state space.


While modality can be classified at a fine level (e.g., epistemic and deontic), we simply determine whether or not a sentence includes a modal marker that is involved in the main predicate of the sentence.

this binary feature is determined based on the existence of a model verb like "can", "shall", "must", and "may" or a phrase like "have to" or "need to".


However, the second sentence with the same verb is not, because the subject is a non-animate abstract concept.

a non-animate noun can hardly constitute an experience.


Let S be an n × n diagonal matrix with S uu = 1 iff node u ∈ V is labeled.

s identifies the labeled nodes in the graph.


The above objective can be rewritten as: From this, we observe that LP-ZGL penalizes any label assignment where two nodes connected by a highly weighted edge are assigned different labels.

lP-ZGl prefers smooth labelings over the graph.


We note that even though the resulting graph consists of edges connecting nodes of different types: cell value nodes to property nodes; the graph-based SSL methods (Section 2) can still be applied on such graphs as a cell value node and a property node connected by an edge should be assigned same or similar class labels.

the label smoothness assumption (see Section 2.2) holds on such graphs.


In this section, we explore whether class-instance assignment can be improved by incorporating new semantic constraints derived from (instance, attribute) pairs.

we experiment with the following type of constraint: two instances with a common attribute are likely to belong to the same class.


A PCFG G consists of: • A finite set of nonterminal symbols N; • A finite set of terminal symbols Σ; • For each A ∈ N, a set of rewrite rules R(A) of the form A → α, where α ∈ (N ∪ Σ) * , and R = ∪ A∈N R(A); • For each rule A → α, a probability θ A→α .

the collection of probabilities is denoted θ, and they are constrained such that: θ is grouped into |N| multinomial distributions.


For two series {a n } and {b n }, let "a n b n " denote that lim n→∞ a n ≥ lim n→∞ b n .


Furthermore, if S i and S j have maximum intersection λ, then any subset of S i also has maximum intersection λ with any subset of S k , and we need not specify those constraints either.

now, let a choke-vertex in the partial order X, be an element u ∈ X such that for every v, w ∈ X where v is a successor of w and u v, we have u w. any chain of successors from elements not after u to elements after u, must pass through u.


However, they do not reveal the entire story.

the apparent superiority of λ = 0 for the synsem_min module should not be taken as indicating that no higher λ could be better: rather, that module includes a very difficult set programming instance on which the solver failed and fell back to GAK.


The results show that our method improves on the baseline by a large margin and that higher performance can be achieved by using more disambiguation information.

using a richer disambiguation context helps to better choose the most appropriate WordNet sense for a Wikipedia page.


In this combination scheme, the output of the global disambiguation system is simply an intersection of the two outputs from the two underlying systems RelCont and TransCont.

we sum up the confidence ranging from 0 to 1 of the two system In-Degree algorithm outputs to obtain a final confidence for each sense, choosing the sense(s) that yields the highest confidences.


In this example, the IG plans to refer to an object (here, a button); and in order to do so, gives a navigation instruction to guide the IF to a convenient location at which she can then use a simple referring expression (RE).

there is an interaction between navigation instructions (intended to manipulate the non-linguistic context in a certain way) and referring expressions (which exploit the non-linguistic context).


One final challenge that arises in our system is to generate the adjectives in the correct order, which on top of semantically valid must be linguistically acceptable.

it is known that some types of adjectives are limited with respect to the word order in which they can occur in a noun phrase.


The module nonetheless naively treats all adjectives as intersective and is not sensitive to the context of their comparison set.

a button cannot be referred to as "the right red button" if it is not the rightmost of all visible objects-which explains the long chain of navigational instructions the system produced in Table 1.


Given a phrase pair bp = (e i , f a i ), where a i defines that the source phrase f a i is aligned to the target phrase e i , the traditional lexicalized reordering model computes the reordering count of bp in the orientation o based on the word alignments of boundary words.

the model collects bilingual phrases and distinguishes their orientations with respect to the previous bilingual phrase into three categories: Using the relative-frequency approach, the reordering probability regarding bp is For a parallel sentence pair, its reordering graph indicates all possible translation derivations consisting of the extracted bilingual phrases.


We also compared the performance of the PCFG approach against n-gram language models.

we tried unigram, bigram and trigram models.


We describe an evaluation of an error recovery policy in the BEETLE II tutorial dialogue system and discuss how different types of interpretation problems affect learning gain and user satisfaction.

the problems arising from student use of non-standard terminology appear to have negative consequences.


Thus, even though in BASE the system did not indicate non-understanding, students were negatively affected.

they were not satisfied with the policy that did not directly address the interpretation problems.


While undoubtedly one of their most important functions, referring expressions can be more than distinctive.

descriptive referring expressions-those that provide additional information not required for distinction-are critical to fluent, efficient, well-written text.


Although these sorts of referring expression have been mostly ignored by researchers in this area 1 , we show in this corpus study that descriptive expressions are in fact quite prevalent: nearly one-fifth of referring expressions in news and narrative are descriptive.

our data, the trained judgments of native English speakers, show that 18% of all distinctive referring expressions in news and 17% of those in narrative folktales are descriptive.


Another key property of human language processing is the fact that it operates with limited memory, and that structures in memory are subject to decay and interference.

the processor is known to incur a distance-based memory cost: combining the head of a phrase with its syntactic dependents is more difficult the more dependents have to be integrated and the further away they are.


13 Beyond this, the ANC project does not provide specific tools for use of the corpus, but rather provides the data in formats suitable for use with a variety of available applications, as described in section 4, together with means to import GrAF annotations into major annotation software platforms.

the ANC project provides plugins for the General Architecture for Text Engineering (GATE) (Cunningham et al., 2002) to input and/or output annotations in GrAF format; a "CAS Consumer" to enable using GrAF annotations in the Unstructured Information Management Architecture (UIMA) (Ferrucci and Lally, 2004); and a corpus reader for importing MASC data and annotations into NLTK 14 .


), so that we get the following order: actP/perc > actP/kNN ≈ actT/kNN > actT/perc where > means "significantly outperforms".

the best method (actT/kNN) outperforms all other methods at p<0.01.


Although the approach is proposed for sequence labeling tasks, it can be easily extended to our structured model.

for each possible predicate sense p of the predicate, we provide N-best argument role assignments using three local factors F P , F A and F P A , and then add scores of the global factor F G , finally select the argmax from them.


Line Bracketing evaluates the word grouping ability of these two kinds of chunks.

detailed phrase types are not considered.


It is likely that the drop in performance for the larger AMM-based measures is due to the AMMs learning the collocations themselves.

the AMMs become rich enough to not only capture the broadly applicative distributional influences of syntax and semantics, but also provide accurate p exp s for individual, distributionally deviant combinations -like collocations.


For addresses from emerging geographies no standard postal address scheme exists and our objective was to produce a postal taxonomy that is useful in standardizing addresses (Kothari et al., 2010).

the experiments were designed to investigate the effectiveness of our approach on noisy terms with lots of variations.


For a node n in the tree, n.t is the term at n, A(n) the ancestors of n, and P (n) the predecessors of n. A TFIT has a root node with the special term ⊥ and the conditional frequency ∞.

the following condition is true for any other node n: each node's term has the highest conditional frequency in the context of the node's ancestors and predecessors.


For our example application, we introduce the following node-level constraint: at each node we only count terms that appear at specific positions in records with respect to the current level of the node.

we slide (or incrementally increase) a window over the address records starting from the end.


Also, to accommodate varying amounts of landmark information we length-normalize the position of terms.

we divide all positions in an address by the average length of an address (which is 10 for our 40 Million addresses).


In general, synonym relations are defined in terms of word sense, not in terms of word form.

synonym relations are usually context or domain dependent.


In the AI step, a lot of syntactic features are extracted to distinguish argument and non-argument.

a binary classifier is trained to classify each argument candidate as either an argument or not.


As arguments should c-command the predicate, a full parser can further prune a majority of useless constituents.

parsing can effectively supply SRL with argument candidates.


We propose an alternative scenario where not all the source sentences are interactively translated by the user.

only those source sentences whose initial fully automatic translation are incorrect, according to some quality criterion, are interactively translated.


We have carried out experimentation ranging τ w between 0.0 and 1.0 and found that this value can be used to solve the above mentioned undesired effect for the MEAN CM.

varying the value of τ w we can stretch the interval in which the transition between the fully automatic SMT system and the conventional IMT system is produced, allowing us to obtain smother transitions.


We explore a particular sparsity bias in dependency grammars that encourages a small number of unique dependency types.

we investigate sparsity-inducing penalties on the posterior distributions of parent-child POS tag pairs in the posterior regularization (PR) framework of Graça et al.


We can achieve the desired bias with a constraint on model posteriors during learning, using the posterior regularization (PR) framework (Graça et al., 2007).

to implement PR we augment the maximum marginal likelihood objective of the dependency model with a term that penalizes head-dependent tag distributions that are too permissive.


The number of unique dependency types is then: Note there is an asymmetry in this count: occurrences of child type c are enumerated with i, but all occurrences of parent type p are or-ed in φ cpi .

φ cpi = 1 if any occurrence of p is the parent of the ith occurrence of c. We will refer to PR training with this constraint as PR-AS.


However, we feel the comparison is not so unfair as we perform only a very limited search of the model-σ space.

the only values of σ we search over are {80, 100, 120, 140, 160, 180}.


The ease of specification translates directly into ease of implementation.

if highquality heuristics are not available, it is often more efficient to implement the 1-best inside pass as an exhaustive dynamic program, as in Huang and Chiang (2005).


Our implementation of KA * constructed 3.41 billion edges, of which about 0.1% were outside edge items or inside derivation items.

the cost of k-best extraction is dwarfed by the the 1-best inside edge computation in both cases.


Most of our experiments are implementations of wrapper methods that call off-the-shelf implementations of supervised learning algorithms.

we have experimented with support vector machines (SVMs), decision trees, bagging and random forests.


The POS-tagging subtask of disambiguation may then be construed as a challenge in its own right: demonstrate effective disambiguation in an unsupervised model.

show that tagging accuracy decreases when the model's disambiguation capacity is removed, by re-labeling all tokens with their modal label, defined above.


We tested our model on the Penn treebank using the same data setup as .

we used only section 2 for training and section 22 (devel) for reporting results.


These improvements are in no way limited to our particular choice of a TSG parsing model, many hierarchical Bayesian models have been proposed which would also permit similar optimised samplers.

models which induce segmentations of complex structures stand to benefit from this work; Examples include the word segmentation model of Goldwater et al.


but not vice versa (we write i ⇒ ( ⇐) ii).

the truth value is preserved if we replace the argument of an upward-entailing operator by a superset (a more general version); in our case, the set 'opium use' was replaced by the superset 'narcotic use'.


A valid question is, does the quality of the results vary with choice of application language?

what happens if we run our algorithm on English?


We contrast it with text categorization and with opinion classification by employing models routinely used for such tasks.

we consider models that use term frequencies as features (usually found to be superior for text categorization) and models that use term absence/presence (usually found to be superior for opinion classification).


So, now we will have (x s , x tp ) and (x sp , x t ) where x (s|t)p are pivot features in the source and the target languages.

when we use the SCL on our problem, the translations are only used to decide if a certain pivot feature occurs or not in the training of the linear predictors.


(2008)) using Conditional Random Fields (CRFs) (Lafferty et al., 2001) to finegrained opinion analysis.

we aim to jointly identify the boundaries of opinion expressions as well as to determine two of their key attributes -polarity and intensity.


Table 4 shows the performance of opinion extraction without matching any attribute.

an extracted opinion entity is counted as correct if it overlaps 4 with a gold standard opinion expression, without checking the correctness of its attributes.


Given the document semantic network represented as a phrase hyper-graph, one way to make better use of the semantic information is to rank phrases with a semi-supervised learning strategy, where the title phrases are regarded as labeled samples, while the other phrases as unlabeled ones.

the information we have at the beginning about how to rank phrases is that the title phrases are the most important phrases.


The hierarchical model jointly optimizes global and domain-specific parameters, using parameters built from pooled data as priors for domain-specific parameters.

instead of using smoothing to encourage parameters to be closer to zero, it encourages domainspecific model parameters to be closer to the corresponding global parameters, while a zero mean Gaussian prior is still applied for global parameters.


(2008) obtained much more value from lexical than nonlexical features (and indeed no value at all from prosodic features), but lexical features have limitations.

they can be domain specific, increase the size of the feature space dramatically, and deteriorate more in quality than other features when automatic speech recognition (ASR) is poor.


Beyond decision detection, DGMs are used for labeling and segmenting sequences of observations in many different fields-including bioinformatics, ASR, Natural Language Processing (NLP), and information extraction.

dynamic Bayesian Networks (dBNs) are a popular model for probabilistic sequence modeling because they exploit structure in the problem to compactly represent distributions over multi-state and observation variables.


For the driving task, we did not find any statistically significant differences between the voice search and dictation conditions.

we could not reject the null hypothesis that the two approaches were the same in terms of their influence on driving performance.


We define the readability score as follows: where, given two adjacent sentences s i and s i+1 , w φ(s i , s i+1 ), which measures the connectivity of the two sentences, is the inner product of w and φ(s i , s i+1 ), w is a parameter vector and φ(s i , s i+1 ) is a feature vector of the two sentences.

the readability score of sentence sequence S is the sum of the connectivity of all adjacent sentences in the sequence.


The references (Human) have fewer sentiments than the summaries generated by our method.

the references included almost as many other sentences (e.g.


The difference is significant as indicated by p < 0.01.

method2 and method2+ tended to select short sentences, which made their summaries less readable.


We suggest that text units should be handled with tools of discourse linguistics too in order to reveal where, within texts, their polarity is best manifested.

we propose to focus on the last sentence of the given text in order to efficiently extract the polarity of the whole text.


include hundreds of subjects in order to draw a profile of polarity evolvement throughout customer reviews.

we present our subjects with sentences in various locations in customer reviews asking them to rate them.


In this paper, we explore a number of methods to automatically generate rationales for documentlevel sentiment classification.

we investigate the use of off-the-shelf sentiment analysis components and lexicons for this purpose.


Intuitively, the contrastive example v ij should not be as informative to the learning algorithm as the original review x i , since one of the supporting regions identified by the human annotator has been deleted.

the correct learned model should be less confident of its classification of a contrastive example vs. the corresponding original example, and the classification boundary of the model should be modified accordingly.


The first approach employs OpinionFinder (Wilson et al., 2005a), an off-the-shelf opinion analysis utility.

1 OpinionFinder identifies phrases expressing positive or negative opinions.


3 Therefore, we next consider an approach that does not rely on supervised learning techniques but instead explores the use of a manually constructed polarity lexicon.

we use the lexicon constructed for Wilson et al.


The accuracy of OPINIONFINDER+HUMANR@SENTENCE reaches 92.50%, which is statistically significantly better than HUMANR (91.61%).

not only can our automatically generated rationales replace human rationales, but they can also improve upon human rationales when they are available.


For hierarchies with tighter estimates, we show that BHA * obtains comparable performance to HA * .

bHA * is more robust to poorly constructed hierarchies.


A projection is relaxed if, for every rule r = A t → B t C t with weight w r the projection r = A t → B t C t has weight w r ≤ w r in G t−1 .

the weight of r is a lower bound on the weight of all rules r in G t which project to r .


Once constructed, this coarse outside score is added to the weight of the conclusion item to form the priority of the resulting item.

the coarse outside score computed by the algorithm plays the same role as a heuristic in standard A * parsing (Klein and Manning, 2003).


Unlike the IN deduction, the OUT deductions only involve items from the same level of the hierarchy.

whereas inside scores wait on coarse outside scores to be constructed, outside scores wait on inside scores at the same level in the hierarchy.


Like ordinary outside costs, bridge outside costs form consistent and admissible estimates of the true Viterbi outside score α(e) of an edge e. Because bridge costs mix rules from the finer and coarser grammar, bridge costs are at least as good an estimate of the true outside score as entirely coarse outside costs, and will in general be much tighter.

that is, we have α(e ) ≤α(e) ≤ α(e) note that the bridge costs become better approximations farther right in the sentence, and the bridge cost of the last word in the sentence is equal to the Viterbi outside cost of that word.


(2007) and T&C08 treat the tasks of preposition selection and error detection as a classification problem.

given the context around a preposition and a model of correct usage, a classifier determines which of the 34 prepositions covered by the model is most appropriate for the context.


Based on the test results of 10 corpora, we show that a PU learning technique outperformed distributional similarity significantly.

the entity set expansion problem is defined as follows: Given a set S of seed entities of a particular class, and a set D of candidate entities (e.g., extracted from a text corpus), we wish to determine which of the entities in D belong to S. we "expand" the set S based on the given seeds.


The probabilistic labels of the spies are then used to decide reliable negatives (RN).

a probability threshold t is determined using the probabilistic labels of spies in SP and the input parameter l (noise level).


Candidate entities: Since we are interested in named entities, we select single words or phrases as candidate entities based on their corresponding part-of-speech (POS) tags.

we choose the following POS tags as entity indicators -NNP (proper noun), NNPS (plural proper noun), and CD (cardinal number).


In contrast, our system involves the user throughout the labeling process, using active learning to intelligently explore the space of similar words.

our system can take advantage of negative examples provided by the user.


In this case, we treat the set of test instances (without their gold labels) as the unlabeled data.

we essentially work in the transductive setting (Vapnik, 2000).


Salience threshold is equal to the salience score of the top k th term, when all terms are sorted in descending order on the basis of their salience scores and a cutoff is defined as a percentage (e.g., top 15%).

if the total number of terms is n, then 100 * k/n must be equal to the percentage cutoff specified.


Secondly, we note that as the percentage cutoff decreases (from 4th column rightwards) the characteristics of the summaries produced by our system are increasingly more similar to those of the human summaries.

within the 100word window imposed by the TAC guidelines, our system is able to fit more (and hence shorter) sentences as we decrease the percentage cutoff.


6 Then, as we decrease the percentage cutoff (from 4th column rightwards) we are increasingly covering more of the content comprised by the human summaries (as far as the ROUGE metrics are able to gauge this, of course).

the improvement of content coverage makes up for the reconstruction error, and at 5% cutoff we already obtain ROUGE scores comparable to LSA extract .


It is interesting to quantify the value of parallel versus non-parallel data for any given MT task.

"how much non-parallel data is worth how much parallel data in order to achieve the same MT accuracy?"


In this section, we first describe an algorithm that attaches function words to a packed forest guided by target chunk information.

given a triple ⟨F S , T, A⟩, namely an aligned (A) source forest (F S ) to target sentence (T ) pair, we 1) tailor the alignment A by removing the alignments for target function words, 2) seek attachable nodes in the source forest F S for each function word, and 3) construct a derivation forest by topologically travers-ing F S .


A simple yet effective modification is that, during rule extraction, we account for multiple interpretations of both aligned and unaligned target function words.

we chose to loose the ambiguous alignments for all of the target function words.


In the second phase, the output of the MT decoder is then tagged with a sequence of abstract suffix tags.

the output of the MT decoder is a sequence of complex stems denoted by x and the output is a sequence of suffix class tags denoted by y.


The result reported here is that when k is 10.

we tested how the number of visited nodes changes according to three factors: maximum number of applicable rules in a transformation, vocabulary size and rule set size.


We to refer to the individual components of a path p. In addition, define S N to be the set of states in S of the form a, b (as opposed to the form c ↓ or c ↑ where c ∈ V L ).

we now define a new index set, I = V ∪ E ∪ S N ∪ P, adding variables y s for s ∈ S N , and y p for p ∈ P. If we take Y ⊂ {0, 1} |I| to be the set of valid derivations, the optimization problem is to find y * = arg max y∈Y f (y), where f (y) = θ • y, that is, we might define θ s = 0 for all s, and The y v and y e variables form a valid derivation in the original hypergraph.


For a given y ∈ Y ′ , for any v with y v = 1, we can recover the previous two leaves (the trigram ending in v) from either the path variables y p , or the hypergraph variables y e .

define v −1 (v, y) to be the leaf preceding v in the trigram path p with y p = 1 and v 3 (p) = v, and v −2 (v, y) to be the leaf two positions before v in the trigram path p with y p = 1 and v 3 (p) = v. Similarly, define v ′ −1 (v, y) and v ′ −2 (v, y) to be the preceding two leaves under the y e variables.


However, vertical search engines are essentially rankers that given a query, return a sorted list of (pointers to) entities that are related to the query.

they do not expose actual association scores, which is a key contribution of our work, nor do they operate on general search queries.


Unlike global models, local models leverage relationships between documents in a corpus.

they rely on a graph structure that represents document similarity.


Although our model generalizes to any entity domain, we focus now on a product domain.

our universe of entities, E, consists of the entities in a large commercial product catalog, for which we observe query-click-product clicks, C e , from the vertical search logs.


As expected, our smoothing models have little improvement on the head-sensitive metric (M SE W ) relative toP mle .

,P hybr performs nearly identically toP mle on the head.


Recently, several hashing methods have been proposed.

simHash (sH) (Charikar M.s., 2002) uses random projections to hash data.


In this section, we present our hashing method, named Semi-Supervised SimHash ( Given the labeled data X L , we construct two sets, attraction set Θ a and repulsion set Θ r .

any pair Unlike previews works that attempt to find L optimal hyperplanes, the basic idea of S 3 H is to fix L random hyperplanes and to find an optimal feature-weight vector to relocate the objects such that similar objects have similar codes.


Finally, we obtain the total objective: where g i,l and d i,l denote the l-th column of G i and D i respectively, and ψ(t) is a piece-wise linear function defined as: This relaxation has a good intuitive explanation.

similar objects are desired to not only have the similar fingerprints but also have sufficient large projection magnitudes, while dissimilar objects are desired to not only differ in their fingerprints but also have large projection margin.


To objectively reflect the performance of S 3 H, we evaluate our S 3 H with and without Feature Contribution Calculation algorithm (FCC) (Algorithm 1).

fCC-free S 3 H (denoted as S 3 H f ) is just a simplification when Gs in S 3 H are simply set to Ds.


, q n ), our goal is to annotate it with an appropriate set of linguistic structures Z Q .

in this work, we assume that the set Z Q consists of shallow sequence annotations z Q , each of which takes the form each symbol ζ i ∈ z Q annotates a single query term.


In this example, Most previous work on query annotation makes the independence assumption -every annotation z Q ∈ Z Q is done separately from the others.

it is assumed that the optimal linguistic annotation z * (I) Q is the annotation that has the highest probability given the query Q, regardless of the other annotations in the set Z Q .


The reason is that KL conducts weighting by density function ratio which is sensitive to the data scale.

after document feature aggregation, the number of query feature vectors in all adaptation tasks is no more than 150 in source and target domains.


positive or negative sentiment) given a small set of labeled data for the source domain, and unlabeled data for both source and target domains.

no labeled data is provided for the target domain.


We then apply a simple word filter based on POS tags to select content words (nouns, verbs, adjectives, and adverbs).

previous work has identified adjectives as good indicators of sentiment (Hatzivassiloglou and McKeown, 1997;Wiebe, 2000).


Note that relatedness values computed according to Equation 2 are sensitive to sentiment labels assigned to reviews in the source domain, because co-occurrences are computed over both lexical and sentiment elements extracted from reviews.

the relatedness of an element u to another element v depends upon the sentiment labels assigned to the reviews that generate u and v. This is an important fact that differentiates our sentiment-sensitive thesaurus from other distributional thesauri which do not consider sentiment information.


The values of the first N dimensions that correspond to unigrams and bigrams w i that occur in the review d are set to d i , their frequency in d. The subsequent k dimensions that correspond to the top ranked based entries for the review d are weighted according to their ranking score.

we set the value of the r-th ranked base entry v r d to 1/r.


The results returned by our method are comparable to state-ofthe-art techniques such as SCL-MI and SFA.

the differences between those techniques and our method are not statistically significant.


In this paper, we propose to improve targetdependent sentiment classification of tweets by using both target-dependent and context-aware approaches.

the target-dependent approach refers to incorporating syntactic features generated using words syntactically connected with the given target in the tweet to decide whether or not the sentiment is about the given target.


A lot of papers have been published on this topic.

turney (2002) proposes an unsupervised method for classifying product or movie reviews as positive or negative.


(Go et al., 2009;Parikh and Movassate, 2009;Barbosa and Feng, 2010;Davidiv et al., 2010) all follow the machine learning based approach for sentiment classification of tweets.

(Davidiv et al., 2010) propose to classify tweets into multiple sentiment types using hashtags and smileys as labels.


In both the subjectivity classifier and polarity classifier, the same targetindependent feature set is used.

we use two kinds of target-independent features: 1.


In this paper, we rely on the syntactic parse tree to satisfy this need.

for any word stem w i in a tweet which has one of the following relations with the given target T or any from the extended target set, we generate corresponding target-dependent features with the following rules:  w i is a transitive verb and T (or any of the extended target) is its object; we generate a feature w i _arg2.


If we consider that the sentiment of a tweet only depends on its content and immediate neighbors, we can leverage a graph-based method for sentiment classification of tweets.

the probability of a tweet belonging to a specific sentiment class can be computed with the following formula: Where c is the sentiment label of a tweet which belongs to {positive, negative, neutral}, G is the tweet graph, N(d) is a specific assignment of sentiment labels to all immediate neighbors of the tweet, and τ is the content of the tweet.


The tagset we adopted is loosely based on DAMSL (Allen & Core, 1997) and SWBD (Jurafsky et al., 1997), but greatly reduced and also tuned significantly towards dialogue pragmatics and away from more surface characteristics of utterances.

we ask our annotators what is the pragmatic function of each utterance within the dialogue, a decision that often depends upon how earlier utterances were classified.


We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of 'English as a Second or Other Language' (ESOL) examination scripts.

we use rank preference learning to explicitly model the grade relationships between scripts.


For the experiments reported here, we use complexity measures representing the sum of the longest distance in word tokens between a head and dependent in a grammatical relation (GR) from the RASP GR output, calculated for each GR graph from the top 10 parses per sentence.

we extract the mean and median values of these distances per sentence and use the maximum values per script.


In essence, it is the inverse of BLEU since we want to minimize the number of n-gram overlaps between the two sentences.

for source sentence s and candidate sentence c: where N is the maximum n-gram considered and ngram s and n-gram c are the lists of n-grams in the source and candidate sentences, respectively.


This score is similar to the Jaccard distance, except that it excludes n-grams that only appear in the source sentence and not in the candidate sentence.

it rewards candi-dates for introducing new n-grams but not for omitting n-grams from the original sentence.


N-best list search: For each sentence W in document d, find N -best parse trees, and denote T N as the collection of N -best list parse trees for sentences over entire corpus D under model parameter p. 2.

eM update: Perform one iteration (or several iterations) of eM algorithm to estimate model parameters that maximizes N -best-list likelihood of the training corpus D, (a) e-step: Compute the auxiliary function of the N -best-list likelihood with respect to p ′ to get new update for p. Iterate steps (1) and (2) until the convergence of the N -best-list likelihood.


When we train the composite language on 1.3 billion tokens corpus, we have to both aggressively prune the parameters of WORD-PREDICTOR and shrink the order of n-gram and m-SLM in order to store them in a supercomputer having 1000 cores.

for composite 5-gram/4-SLM model, its size is too big to store, thus we use its approximation, a linear combination of 5-gram/2-SLM and 2-gram/4-SLM, and for 5-gram/2-SLM or 2-gram/4-SLM, again we cut off its fractional expected counts that are less than a threshold 0.005, this significantly reduces the number of predictor's types by 85%.


State-of-the-art Machine Translation (MT) systems are making progress to generate more usable translation outputs.

statistical machine translation systems (Koehn et al., 2007;Bach et al., 2007;Shen et al., 2008) have advanced to a state that the translation quality for certain language pairs (e.g.


Although the metric shows an improved correlation with human judgment of translation quality (Callison-Burch et al., 2007;Giménez and Màrquez, 2007;Callison-Burch et al., 2008;Giménez and Màrquez, 2008), it is not commonly used in large-scale MT evaluation campaigns, perhaps due to its high time cost and/or the difficulty of interpreting its score because of its highly complex combination of many heterogenous types of features.

note that the feature based representations of semantic roles used in these aggregate metrics do not actually capture the structural predicate-argument relations.


We run decipherment experiments on different types of letter substitution ciphers (described in Section 2).

we work with the following three ciphers: (a) 414-letter Simple Substitution Cipher (b) 414-letter Homophonic Cipher (with spaces) (c) Zodiac-408 Cipher Methods: For each cipher, we run and compare the output from two different decipherment approaches: 1.


To test the ability of ITM to discover relevant subdivisions in a corpus, we use a dataset with predefined, intrinsic labels and assess how well the discovered latent topic structure can reproduce the corpus's inherent structure.

for a corpus with M classes, we use the per-document topic distribution as a feature vector in a supervised classi-  administration, bush, clinton, war, unite, force, reagan, american, america, make, nation, military, iraq, iraqi, troops, international, country, yesterday, plan .


All Initial runs the model for the only the initial number of iterations (100 iterations in this experiment), while All Full runs the model for the total number of iterations added for the interactive version.

(if there were 21 rounds and each round of interactive modeling added 10 iterations, All Full would have 210 iterations more than All Initial).


we compress the key/value array in blocks of 128 bytes.


The query speed of all models is boosted substantially.

our COMPRESSED implementation with caching is nearly as fast as SRILM-H without caching, and even the already fast HASH implementation is 300% faster in raw query speed with caching enabled.


This selection is based on the results of multiple roll-outs which measure the outcome of a sequence of actions in a simulated game -e.g., simulations played against the game's built-in AI.

starting at state s t , the algorithm repeatedly selects and executes actions, sampling state transitions from T .


The parameters w of Q(s, a) are learned based on feedback from the roll-out simulations.

the parameters are updated by stochastic gradient descent by comparing the current predicted Q(s, a) against the observed utility at the end of each rollout.


Since our model is a non-linear approximation of the underlying action-value function of the game, we learn model parameters by applying non-linear regression to the observed final utilities from the simulated roll-outs.

we adjust the parameters by stochastic gradient descent, to minimize the mean-squared error between the actionvalue Q(s, a) and the final utility R(s τ ) for each observed game state s and action a.


In this paper, we study the use of richer document representations for the AA task.

we consider local histograms over n-grams at the character-level obtained via the locally-weighted bag of words (LOWBOW) framework .


Instead, we rely on particular kernels defined for sets of vectors rather than for a single vector.

we consider kernels of the form (Rubner et al., 2001;Grauman, 2006): where D(P, Q) is the sum of the distances between the elements of the bag of local histograms associated to author P and the elements of the bag of histograms associated with author Q; γ is the scale parameter of K. Let P = {p 1 , .


To obtain a deeper understanding of the nature of deceptive opinion spam, we explore the relative utility of three potentially complementary framings of our problem.

we view the task as: (a) a standard text categorization task, in which we use n-gram-based classifiers to label opinions as either deceptive or truthful (Joachims, 1998;Sebastiani, 2002); (b) an instance of psycholinguistic deception detection, in which we expect deceptive statements to exemplify the psychological effects of lying, such as increased negative emotion and psychological distancing (Hancock et al., 2008;Newman et al., 2003); and, (c) a problem of genre identification, in which we view deceptive and truthful writing as sub-genres of imaginative and informative writing, respectively (Biber et al., 1999;Rayson et al., 2001).


We compare the performance of each approach on our novel dataset.

we find that machine learning classifiers trained on features traditionally employed in (a) psychological studies of deception and (b) genre identification are both outperformed at statistically significant levels by ngram-based text categorization techniques.


Additionally, we make several theoretical contributions based on an examination of the feature weights learned by our machine learning classifiers.

we shed light on an ongoing debate in the deception literature regarding the importance of considering the context and motivation of a deception, rather than simply identifying a universal set of deception cues.


Following the work of Yoo and Gretzel (2009), we compare truthful and deceptive positive reviews for hotels found on TripAdvisor.

we mine all 5-star truthful reviews from the 20 most popular hotels on TripAdvisor 8 in the Chicago area.


Submissions vary quite dramatically both in length, and time spent on the task.

nearly 12% of the submissions were completed in under one minute.


Additionally, to test the extent to which the individual human judges are biased, we evaluate the performance of two virtual meta-judges.

the MAJORITY meta-judge predicts "deceptive" when at least two out of three human judges believe the review to be deceptive, and the SKEP-TIC meta-judge predicts "deceptive" when any human judge believes the review to be deceptive.


While LIWC does not include a text classifier, we can create one with features derived from the LIWC output.

lIWC counts and groups the number of instances of nearly 4,500 keywords into 80 psychologically meaningful dimensions.


In contrast to the other strategies just discussed, our text categorization approach to deception detection allows us to model both content and context with n-gram features.

we consider the following three n-gram feature sets, with the corresponding features lowercased and unstemmed: UNIGRAMS, BIGRAMS + , TRIGRAMS + , where the superscript + indicates that the feature set subsumes the preceding feature set.


genre identification baseline just discussed.

the psycholinguistic approach (LIWC SVM ) proposed in Section 4.2 performs 3.8% more accurately (one-tailed sign test p = 0.02), and the standard text categorization approach proposed in Section 4.3 performs between 14.6% and 16.6% more accurately.


However, best performance overall is achieved by combining features from these two approaches.

the combined model LIWC+BIGRAMS + SVM is 89.8% accurate at detecting deceptive opinion spam.


Finally, we have made several theoretical contributions.

our findings suggest the importance of considering both the context (e.g., BIGRAMS + ) and motivations underlying a deception, rather than strictly adhering to a universal set of deception cues (e.g., LIWC).


Our evaluation results show that both methods significantly outperform the baseline approach that extracts the longest utterances.

we find that incorporating dialogue structure in the graph-based method contributes to the improved system performance.


The goal of our work is to provide a mechanism for review content aggregation that goes beyond numerical scores.

we are interested in identifying fine-grained product properties across reviews (e.g., battery life for electronics or pizza for restaurants) as well as capturing attributes of these properties, namely aggregate user sentiment.


Ideally, we would like to marginalize out nuisance random variables and distributions.

we approximate the full model posterior using variational inference: 1 where ψ, θ P , φ denote the collection of latent distributions in our model.


3 To get a sense of the relative need for lexical normalisation, we perform analysis of the distribution of OOV words in different text types.

we calculate the proportion of OOV tokens per message (or sentence, in the case of edited text), bin the messages according to the OOV token proportion, and plot the probability mass contained in each bin for a given text type.


The (word1,word2,position) features are sparse and sometimes lead to conservative results in illformed word detection.

without valid features, the SVM classifier tends to label uncertain cases as correct rather than ill-formed words.


Therefore, in this paper, we propose to study the novel problem of extracting topical keyphrases for summarizing and analyzing Twitter content.

we extract and organize keyphrases by topics learnt from Twitter.


Taking the topic of "electronic products" as an example, the word "juice" may co-occur frequently with a good keyword "apple" for this topic because of Apple electronic products, so "juice" may be ranked high by this context-free co-occurrence edge weight although it is not related to electronic products.

context-free propagation may cause the scores to be off-topic.


For any assignment to (a, b, c) with non-zero probability, c must encode a one-to-one phrase alignment with a maximum phrase length of 3.

any word in either sentence can align to at most three words in the opposite sentence, and those words must be contiguous.


The dual decomposition inference approach allows us to exploit this sub-graph structure .

we can iteratively apply exact inference to the subgraph problems, adjusting their potentials to reflect the constraints of the full problem.


In fact, we can make a stronger claim: we can reuse the Viterbi inference algorithm for linear chain graphical models that applies to the embedded directional HMM models.

we can cast the optimization of Equation 5 as In the original HMM-based aligner, the vertex potentials correspond to bilexical probabilities.


Other approaches to generating one-to-one phrase alignments are generally more expensive.

an ITG model requires O(|e| 3 • |f | 3 ) time, whereas our algorithm requires only Moreover, our approach allows Markov distortion potentials, while standard ITG models are restricted to only hierarchical distortion.


(1985), reducing from a graph problem known as bisection width (see also Monien and Sudborough (1988)).

the authors construct a graph G ′ of maximum degree three and an integer k ′ from an input graph G = (V, E) with an even number n of vertices and an integer k, such that mcw(G ′ ) ≤ k ′ if and only if the bisection width bw(G) of G is not greater than k, where The graph G ′ has vertices of degree two and three only, and it is based on a grid-like gadget R(r, c); see Figure 5.


The objective we maximize in performing the initial extraction is different from the one used in extractive summarization.

we pick an extraction that maximizes s∈y b∈s v b .


Because documents have pre-defined structures (e.g., sections, paragraphs, sentences) for different levels of concepts in a hierarchy, most recent summarization work has focused on structured probabilistic models to represent the corpus concepts (Barzilay et al., 1999;Daumé-III and Marcu, 2006;Eisenstein and Barzilay, 2008;Tang et al., 2009;Chen et al., 2000;Wang et al., 2009).

(Haghighi and Vanderwende, 2009;Celikyilmaz and Hakkani-Tur, 2010) build hierarchical topic models to identify salient sentences that contain abstract concepts rather than specific concepts.


Submodular functions are those that satisfy the property of diminishing returns: .

the incremental "value" of v decreases as the context in which v is considered grows from A to B.


Interestingly, the gain function defined in the original MMR paper (Carbonell and Goldstein, 1998) satisfies diminishing returns, a fact apparently unnoticed until now.

carbonell and Goldstein (1998) define an objective function gain of adding element k to set S (k / ∈ S) as: where Sim 1 (s k , q) measures the similarity between unit s k to a query q, Sim 2 (s i , s k ) measures the similarity between unit s i and unit s k , and 0 ≤ λ ≤ 1 is a trade-off coefficient.


By definition (Lin, 2004), ROUGE-N is the n-gram recall between a candidate summary and a set of reference summaries.

let S be the candidate summary (a set of sentences extracted from the ground set V ), c e : 2 V → Z + be the number of times n-gram e occurs in summary S, and R i be the set of n-grams contained in the reference summary i (suppose we have K reference summaries, i.e., i = 1, • • • , K).


We therefore propose to positively reward diversity instead of negatively penalizing redundancy.

we model the summary quality as where L(S) measures the coverage, or "fidelity", of summary set S to the document, R(S) rewards diversity in S, and λ ≥ 0 is a trade-off coefficient.


Assume k 1 is already in the summary set S. Greedily selecting the next element will choose k 3 rather than k 2 since √ 13 < 2 + 2.

adding k 3 achieves a greater reward as it increases the diversity of the summary (by choosing from a different cluster).


there are 5 sentences in each cluster on average.


To better estimate of the relevance between query and sentences, we further expanded sentences with synonyms and hypernyms of its constituent words.

part-of-speech tags were obtained for each sentence using the maximum entropy part-of-speech tagger (Ratnaparkhi, 1996), and all nouns were then expanded with their synonyms and hypernyms using WordNet (Fellbaum, 1998).


We employ a simple learning framework that is similar to the hierarchical learning strategy as described in Section 3.1.

we first train a binary classifier to distinguish between relation instances and non-relation instances.


In contrast to previous work, our approach learns from domain-independent meta-constraints on relation expression, rather than supervision specific to particular relations and their instances.

we leverage the linguistic intuition that documents in a single domain exhibit regularities in how they express their relations.


We also study the importance and effectiveness of the declaratively-specified constraints.

we find that a small set of declarative constraints are effective across domains, while additional domainspecific constraints yield further benefits.


Separation The separation constraint encourages diversity in the discovered relation types by restricting the number of times a single word can serve as either an indicator or part of the argument of a relation instance.

we require that every token of the corpus occurs at most once as a word in a relation's argument in expectation.


Our second modification is to do a Viterbi approximation, by replacing the expectations with maximizations.

we compute the most likely sentence extractions for the label facts arg max z p(z|x i , y i ; θ) and the most likely extraction for the input, without regard to the labels, arg max y,z p(y, z|x i ; θ).


Predicting the most likely joint extraction arg max y,z p(y, z|x; θ) can be done efficiently given the structure of our model.

we note that the factors Φ join represent deterministic dependencies between Z and Y, which when satisfied do not affect the probability of the solution.


The constrained structures allow us to jointly entertain argument candidates and relations built with them as arguments.

we show that considering argument candidates which otherwise would have been discarded (provided they exist in syntactico-semantic structures), we reduce error propagation along a standard pipeline RE architecture, and that this joint inference process leads to improved RE performance.


Addressing such situations is the main focus of this work.

we address the following question: In the absence of a pivot resource rich language is it possible for two resource deprived languages to mutually benefit from each other's annotated data?


Instead of using monolingual bootstrapping, we use bilingual bootstrapping via parameter projection.

the parameters learned from the annotated data of L 1 (and L 2 respectively) are projected to L 2 (and L 1 respectively) and the projected model is used to tag the untagged instances of L 2 (and L 1 respectively).


This is intuitive, because, as the seed size increases the algorithm is able to see more and more tagged instances in its self corpora and hence does not need any assistance from the other language.

the annotated data in L 1 is not able to add any new information to the training process of L 2 and vice versa.


Co-training is a semi-supervised learning algorithm for data sets where each instance can be classified from two (or more) independent sets of features.

the features of each instance x i can be partitioned into two views, , and there exist functions in each view, f 1 , f 2 , such that f 1 (x 1 i ) = f 2 (x 2 i ) = y i .


Although this experiment is mainly designed to evaluate ConceptResolver's ability to detect synonyms, it is somewhat affected by the word sense induction process.

the gold standard clustering contains noun phrases that refer to multiple concepts within the same category.


And the falsity of any of them is sufficient to make (1) true.

(1) would be true if nobody eats, cows don't eat or meat is not eaten.


This information is retrieved from the store when the quantifier in column i is executed.

the restrictor is A = Σ (d.b i ) and the nuclear scope is B = Σ (d[i, −(i, ø)]).


Essentially, T d (a, b) allows us to insert up to d trace predicates between the roots of a and b.

this is useful for modeling relations in noun compounds (e.g., California cities), and it also allows us to underspecify L. our L will not include verbs or prepositions; rather, we rely on the predicates corresponding to those words to be triggered by traces.


we truncate each C i,j to a maximum of K candidates sorted by decreasing score based on parameters θ. LetZ L,θ (x) be this approximation of Z L (x).


The number of latent HMM states for each language in our experiments was set to the number of fine tags in the language's treebank.

the set of hidden states F was chosen to be the fine set of treebank tags.


Each syntactic language model state is a random variable store, containing a slice of random variables from the HHMM.

,τ t h contains those random variables s 1..D t that maintain distributions over syntactic elements.


By maintaining these syntactic random variable stores, each hypothesis has access to the current language model probability for the partial translation ending at that hypothesis, as calculated by an incremental syntactic language model defined by the HHMM.

the random variable store at hypothesis h provides P(τ t h ) = P(e h 1..t , s 1..D 1..t ), where e h 1..t is the sequence of words in a partial hypothesis ending at h which contains t target words, and where there are D syntactic random variables in each random variable store (Eq.


It can be seen that, as expected, the discounts for short phrases are lower than those of long phrases.

phrase pairs of length up to six (for example, |e| = 3, |f | = 3) are given discounts of nearly zero while larger phrases are more heavily discounted.


This indicates potential gains to be provided by length-based parameter tuning were outweighed by losses due to the increased complexity of the model.

we believe the necessity to combine probabilities from multiple P t,l models into a single phrase table may have resulted in a distortion of the phrase probabilities.


Following (Berger and Lafferty, 1999), we restrict our attention to those phrase translations consistent with a good wordlevel alignment.

we make the following contributions: • we formulate the question retrieval task as a phrase-based translation problem by modeling the contextual information (in Section 3.1).


Our hypothesis, for which we provide empirical justification, is that in cases where there is no theoretically preferred annotation, NED prefers the structures that are more learnable by DMV.

nED gives higher scores to the annotations that better fit the assumptions and modeling decisions of DMV, the model that lies in the basis of the parsing algorithms.


This equivalence reveals a deep correspondence between the arc-standard model and bilexicalized context-free grammar, and, via results by Eisner and Satta (1999), to head automata.

eisner's and Satta's "hook trick" can be applied to our tabulation to reduce its runtime to O.jwj 4 /.


the feature model˚0 given above is "monotonic", but cannot be tabulated, neither in our nor in their framework.


The crucial observation now is that the concept of push computations and the approach to their tabulation that we have taken for the arc-standard system can easily be generalized to other transition systems whose transitions are of the type shift or reduce.

the proof of the correctness of our deduction system that we gave in Section 3 still goes through if instead of sh we write "shift" and instead of la and ra we write "reduce".


This corpus allows us to efficiently access huge amounts of web-derived information in a compressed way, though in the process it limits us to local queries.

we only use counts of n-grams of the form x y where the gap length is ≤ 3.


The choice of this model is driven by the observation that the repairs frequently seem to be a "rough copy" of the reparandum, often incorporating the same or very similar words in roughly the same word order.

they seem to involve "crossed" dependencies between the reparandum and the repair.


Formally, we define the document coding task as follows: given a set of documents X and a set of available codes C, assign to each document x i a subset of codes C i ⊂ C. We also assume access to a (noisy) mechanism to detect candidate triggers in a document.

we will assume that an (incomplete) dictionary D(c) exists for each code c ∈ C, which lists specific code terms asso-ciated with c. 1 To continue our running example: D(493.9) would include the term "allergic bronchitis".


For example, the ICD-9-CM coding rules state that no symptom codes should be given to a document if a definite diagnosis is present.

if a document is coded with pneumonia, it should not be coded with a fever or cough.


The medical domain is rich with manually-created and carefullymaintained knowledge resources.

the ICD-9-CM coding guidelines come with an index file that contains hundreds of thousands of terms mapped to corresponding codes.


We transform this into a sequence to be tagged using the following steps: Ordering: The candidate code sequence is presented in reverse chronological order, according to when their corresponding trigger terms appear in the document.

the last candidate to be detected by the dictionary will be the first code to appear in our candidate sequence.


The training and test sets have similar, very imbalanced distributions of codes.

all codes in the test set have at least one training example.


syntactic) differences between sentences, we employ a rudimentary dependency-graph alignment module, similar to those more commonly used in the textual entailment community.

we seek answers to the following questions.


Given a pair of two texts as input, typically referred to as text and hypothesis, a textual entailment system automatically finds if the hypothesis is entailed by the text.

the entailment-related works that are most similar to our own are the graph matching techniques proposed by  and Rus et al.


This paper explores a text classification problem we will call lect modeling, an example of what has been termed computational sociolinguistics.

we use machine learning techniques to identify social power relationships between members of a social network, based purely on the content of their interpersonal communication.


Exact inference in these models is intractable and a number of approximate inference schemes (McCallum et al., 2009;Rush et al., 2010;Martins et al., 2010) may be used.

markov chain monte Carlo (mCmC) based inference has been found to work well in practice.


However, the super exponential nature of the hypothesis space in cross-doc coreference renders this algorithm computationally unsuitable for large scale coreference tasks.

fruitful proposals (that increase the model score) are extremely rare, resulting in a large number of proposals that are not accepted.


I-BART's results are clearly better than those with both baselines, but also clearly in-  Table 6: Results for overall coreference: Japanese (MUC score) ferior to the results obtained with our models.

the effect of introducing the separated model with ILP+BF+SUBJ is more significant when using the system detected mentions; it obtained performance more than 13 points better than I-BART when the model referred to the system detected mentions.


We seek answers to these questions by conducting a systematic evaluation of different world knowledge sources for learning-based coreference resolution.

we (1) derive world knowledge from encyclopedic sources that are underinvestigated for coreference resolution, including FrameNet (Baker et al., 1998) and YAGO (Suchanek et al., 2007), in addition to coreference-annotated data and unannotated data; (2) incorporate such knowledge as features into a richer baseline feature set that we previously employed (Rahman and Ng, 2009); and (3) evaluate their utility using two coreference models, the traditional mention-pair model (Soon et al., 2001) and the recently developed cluster-ranking model (Rahman and Ng, 2009).


We evaluate on documents that are coreferenceannotated using both the ACE annotation scheme and the OntoNotes annotation scheme, so that we can examine whether the usefulness of our world knowledge sources is dependent on the underlying coreference annotation scheme.

our data set is composed of the 410 English newswire articles that appear in both OntoNotes-2 and ACE 2004/2005.


Training a mention extractor on these NPs implies that we are learning to extract non-singleton NPs, which are typically much smaller in number than the entire set of NPs.

doing so could substantially simplify the coreference task.


Consequently, we follow the approach adopted by traditional learning-based resolvers and employ an NP chunker to extract NPs.

we use the markable identification system in the Reconcile resolver (Stoyanov et al., 2010) to extract NPs from the training and test texts.


1 After training, the classifier is used to identify an antecedent for an NP in a test text.

each NP, NP k , is compared in turn to each preceding NP, NP j , from right to left, and NP j is selected as its antecedent if the pair is classified as coreferent.


(2008)) and the mentionranking model (e.g., Denis and Baldridge (2008)).

the CR model ranks the preceding clusters for an active NP so that the highest-ranked cluster is the one to which the active NP should be linked.


(2007), we create cluster-level features from the relational features in our feature set using four predicates: NONE, MOST-FALSE, MOST-TRUE, and ALL.

for each relational feature X, we first convert X into an equivalent set of binary-valued features if it is multivalued.


We train a cluster ranker to jointly learn anaphoricity determination and coreference resolution using SVM light 's ranker-learning algorithm.

for each NP, NP k , we create a training instance between NP k and each preceding cluster c j using the features described above.


Briefly, B 3 computes the R and P values of each NP and averages these values at the end.

for each NP, NP j , B 3 first computes the number of common NPs in KP j and RP j , the clusters containing NP j in KP and RP , respectively, and then divides this number by |KP j | and |RP j | to obtain the R and P values of NP j , respectively.


Hence, if the response is generated using goldstandard NPs, then every NP in the response is mapped to some NP in the key and vice versa.

there are no twinless (i.e., unmapped) NPs (Stoyanov et al., 2009).


Note that by construction, exactly one of these features will have a non-zero value.

for the CR model, we extend their definitions so that they can be computed between an NP, NP k , and a preceding cluster, c. the value of a feature is 1 if and only if its value between NP k and one of the NPs in c is 1 under its original definition.


The noun-pair-based features for the CR model can be generated using essentially the same method.

since each instance now corresponds to an NP, NP k , and a preceding cluster, c, we can generate a noun-pair-based feature by applying the above method to NP k and each of the NPs in c, and its value is the number of times it is applicable to NP k and c. As discussed above, features encoding the semantic roles of two NPs and the relatedness of the associated verbs could be useful for coreference resolution.


Rather than encoding verb relatedness, we may replace verb relatedness with the verbs themselves in these features, and have the learner learn directly from coreference-annotated data whether two NPs serving as the objects of decry and denounce are likely to be coreferent or not, for instance.

assuming that NP j and NP k are the arguments of two stemmed predicates, pred j and pred k , in the training data, we create five features as follows.


As described in Section 2, we use as our evaluation corpus the 411 documents that are coreferenceannotated using the ACE and OntoNotes annotation schemes.

we divide these documents into five (disjoint) folds of roughly the same size, training the MP model and the CR model using SVM light on four folds and evaluate their performance on the remaining fold.


Table 2 shows the results obtained when the different types of features are added to the Baseline one after the other.

we add the feature types in this order: YAGO TYPE, YAGO MEANS, Noun Pairs, FrameNet, Verb Pairs, and Appositives.


Maletti (2010) argued the algorithmical advantages of MBOT over STSG and proposed MBOT as an implementation alternative for STSG.

the training procedure would train STSG; i.e., it would not utilize the additional expressive power of MBOT.


Each such pair is called a pre-translation and the rank rk( t, u ) the pre-translation t, u is |u|.

the rank of a pre-translation equals the number of output trees stored in it.


In other words, the rank of a pre-translation equals the number of output trees stored in it.

given a pre-translation t, u ∈ T Σ ×T k ∆ and i ∈ [k], we call u i the i th translation of t. An alignment for the pre-translation t, u is an injective mapping ψ : an alignment should request each translation of a particular subtree at most once and if it requests the i th translation, then it should also request all previous translations.


We transform the source tree phrasal structure into a set of simpler structures, expose such decisions to the decoding process, and find the least expensive transformation operation to better model word reordering.

we integrate synchronous binarizations, verb regrouping, removal of redundant parse nodes, and incorporate a few important features such as translation boundaries.


These two nodes are aligned in a "insideout" fashion, and none of them can be generalized into a nonterminal to be rewritten in a PSCFG rule.

the phrasal brackets induced from NP* and PP* operators for removing nodes/edges Examples remove unary nodes  Xiong et al.


We chose the testsets of newswire and weblog genres from MT08 and DEV10 1 .

we choose MT08 to enable the comparison of our results to the reported results in NIST evaluations.


For our Arabic parser, we have a labeled F-measure of 78.4%, and POS tag accuracy 94.9%.

we'll evaluate model p c (t|γ,m) in Eqn.


A hierarchy of PYPs can be formed by making the base distribution of a PYP another PYP, following a semantics whereby whenever a customer sits at an empty table in a restaurant, a new customer is also said to enter the restaurant for its base distribution.

each table at one level is equivalent to a customer at the next deeper level, creating the invariants: where u = t l−1 indicates the unigram backoff context of h. The recursion terminates at the lowest level where the base distribution is static.


This model may be refined by incorporating richer features and improved decoding.

we would like to experiment with higher-order features ( §6), and with maximum a posteriori decoding, via max-product BP or (relaxed) integer linear programming.


Our dataset (for the email domain) is obtained from the Enron email collection (Bekkerman et al., 2004;Klimt and Yang, 2004).

we chose the three users with the largest number of emails.


In this set of experiments, we examine the feasibility of that approach.

we consider the user Farmer and observe the accuracy of our algorithm with two sets of parameters: the first, denoted by MaxPaths in Figures 5(a) and 5(b), is learned within the Farmer dataset, and the second, denoted by MaxPaths , is learned within the Kaminski dataset.


In future work, we plan to explore how to utilize additional domain knowledge to better estimate the correlation between words.

from available auxiliary data (Fagin et al., 2010) and tools like information extraction (Chiticariu et al., 2010), we can infer and utilize type information from the corpus (Li et al., 2006b;Zhu et al., 2007).


Our key contribution here is a novel, simple, and elegant adaptation method within the framework of the Naïve Bayes algorithm, which yields even greater performance gains.

we show how the error patterns of the non-native writers can be viewed as a different distribution on candidate priors in the confusion set.


In our model, the context of each name mention m is the word window surrounding m, and the window size is set to 50 according to the experiments in (Pedersen et al., 2005).

the context knowledge of an entity e is encoded in an unigram language model: where P e (t) is the probability of the term t appearing in the context of e. In our model, the term may indicate a word, a named entity (extracted using the Stanford Named Entity Micheal Jordan NULL Recognizer 4 ) or a Wikipedia concept (extracted using the method described in (Han and Zhao, 2010)).


We believe that first identifying the particular argumentation scheme that an argument is using will help to bridge the gap between stated and unstated propositions in the argument, because each argumentation scheme is a relatively fixed "template" for arguing.

given an argument, we first classify its argumentation scheme; then we fit the stated propositions into the corresponding template; and from this we infer the enthymemes.


Whereas previous studies in generation mainly used human evaluation to compare different systems, or to correlate human and automatic evaluations, our primary interest is the agreement or correlation between human rankings.

we explore the hypothesis that this agreement is higher in certain contexts than in others.


The question that we are asking instead focuses on how speakers view their authority as a source of information about the topic of the discourse.


However, adapting dialogue act tags to the question of how speakers position themselves is not straightforward.

the granularity of these tagsets, which is already a highly debated topic (Popescu-Belis, 2008), is not ideal for the task we have set for ourselves.


This theory makes use of discourse segmentation.

research in the SFL community has focused on intra-segment structure, and empirical evidence from this research has shown that exchanges between speakers follow a very specific pattern: to say, each segment contains a primary move (a K1 or an A1) and an optional preceding secondary move, with other non-contentful moves interspersed throughout.


The second system (T w rl−6 ) uses no distortion and gap distance features, but applies a hard constraint which limits reordering to no more than 6  positions.

we do not extend hypotheses that are more than 6 words apart from the first word of the left-most gap during decoding.


2 When uncertainty about surface input is introduced, howeverdue to visual noise, imperfect memory representations, and/or beliefs about possible speaker erroranalyses come into play in which some parts of the true string are treated as if they were absent.

because the two sentences are perceptual neighbors, the pre-disambiguation garden-path analysis of (2) may be entertained in (3).


w * and w must be conditionally independent given I since w * is not observed by the comprehender, giving us (through Bayes' Rule): For present purposes we constrain the comprehender's model of noise so that T and I are conditionally independent given w, an assumption that can be relaxed in future work.

3 This allows us the further simplification to a comprehender's average inferences about sentence identity and structure involve a tradeoff between (i) the prior probability of a grammatical derivation given a speaker's linguistic knowledge and (ii) the fidelity of the derivation's yield to the true sentence, as measured by a combination of true noise processes and the comprehender's beliefs about those processes.


Future work will focus on increasing the coverage of the grammars, as well as the number of alternative options explored.

both approaches for auxiliaries should be compared using alternative analyses for verb-second word order found in other HPSG-based grammars, such as the GG (Müller and Kasper, 2000;Crysmann, 2005), Grammix (Müller, 2009;Müller, 2008) and Cheetah (Cramer and Zhang, 2009) for German, and Alpino (Bouma et al., 2001) for Dutch.


In addition to providing a clearer picture of auxiliary structures, these extensions will also lead to a better insight into efforts involved in using grammar generation to explore alternative versions of a grammar over time.

it should provide an indication of the feasibility of maintaining a higher number of competing analyses as the grammar grows.


For unsupervised learning, the expectation is that the model will learn to generalize on phrasal boundaries.

the models will learn to associate terms like the and a, which often occur at the beginnings of sentences and rarely at the end, with the tag B, which cannot occur at the end of a sentence.


The greater context provided by right branching rules allows the model to explicitly estimate separate probabilities for P (I → recent I) versus P (I → recent O).

recent within a chunk versus ending a chunk.


We use one sentential template and an SVM classifier.

we first collect definition sentence candidates by a template "ˆNP .


Note that values of the features f8 and f9 are weighted with −1, since they indicate the unlikelihood.

we first randomly sampled 30,000 definition sentence pairs from the 29,661,812 pairs, and collected 3,000 candidate phrase pairs that had the highest likelihood from them.


The unsupervised method works in the same way as the supervised one, except that it ranks candidate phrase pairs by the sum of all 17 feature values, instead of the distance from the SVM's hyperplane.


The features used with our methods were selected from all of the 78 features mentioned in Section 3.2 so that they performed well for Web sentence pairs.

the features were selected by ablation tests using training data that was tailored to Web sentence pairs.


We represent each word by its context in the cluster and find the similarity of such contexts.

each word w i is represented by a bag of words, i , that have a surface distance of 3 or smaller to w i anywhere in the cluster.


Particularly, each word w i is represented by a bag of words, i , that have a surface distance of 3 or smaller to w i anywhere in the cluster.

i contains any word that co-occurs with w i in a 4-gram in the cluster.


Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998) uses the pairwise cosine similarity matrix and greedily chooses sentences that are the least similar to those already in the summary.

where A is the set of documents in the summary, initialized to A = ∅.


The basic assumption in DivRank is that the transition probability from a node to other is reinforced by the number of previous visits to the target node (Mei et al., 2010).

let's assume p T (u, v) is the transition probability from any node u to node v at time T .


We measure lexical similarity between two clusters through cosine similarity.

each cluster is represented as a vector whose components correspond to the occurrence frequencies of the argument head words in the cluster.


And They mainly appear in Transport event mentions as Place, and in Attack as Destination.

the Exploding entities only occur as Instrument and Artifact respectively with the probability 0.986 and 0.014.


On the basis, given an entity mention in the candidate and its type (see the pretreatment process in section 5.1), the argument classifier could predict whether other entity mentions co-occur with it in such a domain, if yes, all the mentions will be the arguments of the candidate.

if we know an entity of a certain type participates in some event, we will think of what entities also should participate in the event.


Both approaches, however, generally tackle event recognition and role filler extraction at the same time.

most event extraction systems primarily recognize contexts that explicitly refer to a relevant event.


Its precision of 63% means that some documents that are not event narratives will also get additional scrutiny, but information will be extracted only if both the role-specific sentence recognizer and NP extractors believe they have found   For comparison's sake, we also created a document classifier to identify domain-relevant documents.

we trained a classifier to determine whether a document is relevant to the domain of terrorism, irrespective of the style of the document.


We demonstrate that the specific way of counting has a great impact on result quality, and that the state-ofthe-art counting methods are not optimal.

we examine the problem from the viewpoint of probabilistic evidence combination and find that the probabilistic assumption behind simple counting is the statistical independence between the observations of supporting sentences.


We start from the simple case of independent supporting sentences.

by applying bayes rule, we get, Here G(A|E) represents the log-probability-gain of A given E, with the meaning of the gain in the log-probability value of A after the evidence E is observed (or known).


Pairwise modeling may fail to produce coherent partitions.

if we link results of pairwise decisions to each other, there may be conflicting coreferences.


Emotion plays a vital role in human interactions.

the role of facial expressions in humanhuman dialogue is widely recognized.


The availability of learner corpora is still somewhat limited despite the obvious usefulness of such data in conducting research on natural language processing of learner English in recent years.

learner corpora tagged with grammatical errors are rare because of the difficulties inherent in learner corpus creation as will be described in Sect.


However, none of them are designed to be used for learner corpora.

a variety of linguistic phenomena occur in learner corpora which the existing annotation schemes do not cover.


We demonstrate a variety of mechanisms that increase the translation quality to near professional levels.

we solicit redundant translations and edits to them, and automatically select the best output among them.


(Each Turker is identifiable only through an anonymous ID like A23KO2TP7I4KK2.)

no information is available about a worker's educational background, skills, or even native language(s).


We also evaluate Turker translation quality by using them as reference sets to score various submissions to the NIST MT evaluation.

we measure the correlation (using Pearson's r) between BLEU scores of MT systems measured against nonprofessional translations, and BLEU scores measured against professional translations.


To that end, we first investigate completely eliminating the editing phase, and considering only unedited translations.

the selection will be over a group of four English sentences rather than 14 sentences.


The transformation procedure is summarized in Algorithm 1.

line 2 of Algorithm 1 find the lowest parent of an empty element that spans at least one non-trace word.


the phrase translation and lexical probabilities for the phrase pairs <ē m ,f ′ m > derived using the method in Section 3.1.

we use the phrase translation probabilities p(f ′ m |ē m ) and p(ē m |f ′ m ), as well as the lexical translation probabilities p lex (f ′ m |ē m ) and p lex (ē m |f ′ m ) as calculated in (Koehn et al., 2003).


Despite these advantages, system combination may be more appropriate in some settings.

mBR system combination is designed primarily for statistical systems that generate N -best or lattice outputs.


With a belief that a language model that embraces a larger context provides better prediction ability, we learn additional information from training data to enhance conventional n-gram language models and extend their ability to capture richer contexts and long-distance dependencies.

we integrate backward n-grams and mutual information (MI) triggers into language models in SMT.


where the approximation is based on the nth order Markov assumption.

when we predict the current word w i , we only consider the pre- Different from the forward n-gram language model, the backward n-gram language model assigns a probability P b (w m 1 ) to w m 1 by looking at the succeeding context according to For the convenience of training, we invert the order in each sentence in the training data, i.e., from the original order (w 1 ...w m ) to the reverse order (w m ...w 1 ).


We built the sample in order to ensure hapaxes in the whole corpus are hapaxes in all subsets.

we ensured the 431 hapaxes in the evaluation lists are represented in the 500 documents subset.


The final entailment decision for a T/H pair is assigned considering a model learned from the similarity scores based on the identified phrasal matches.

"YES" and "NO" judgements are assigned considering the proportion of words in the hypothesis that are found also in the text.


The first two rows of the table show that pruned paraphrase tables always outperform the other lexical resources used for comparison, with an accuracy increase up to 3%.

we observe that using 0.2 as a pruning threshold provides a good tradeoff between coverage and precision, leading to our best results on both datasets (63.50% for RTE3, and 62.67% for RTE5).


The monolingual classifier can then be used both within and beyond the aligned bitext.

it achieves close to 96% accuracy on both bitext data and on out-of-domain examples in the Treebank.


We propose a novel unsupervised method for separating out distinct authorial components of a document.

we show that, given a book artificially "munged" from two thematically similar biblical books, we can separate out the two constituent books almost perfectly.


One of the advantages of using biblical literature is the availability of a great deal of manual annotation.

we are able to identify synsets by exploiting the availability of the standard King James translation of the Bible into Eng-lish (KJV).


Second, the clusters that we obtain are based on a subset of the full collection of synsets that does the heavy lifting.

formally, we say that a synonym n in synset s is over n is over-represented in C if knowing that n appears in a unit increases the likelihood that the unit is in C, relative to knowing only that some member of n's synset appears in the unit and relative to knowing nothing.


As is evident, in each case the vast majority of verses are correctly assigned and only a small fraction are incorrectly assigned.

we can tease apart the components almost perfectly.


We have found that approximation based on the truncated singular value decomposition provides an effective trade-off of time for space.

we compute X T X ≈ Lower truncation levels are less accurate, but are faster and require less space: for K singular values, the storage cost is O(KP ), instead of O(P 2 ); the time cost increases by a factor of K. This approximation was not necessary in the experiments presented here, although we have found that it performs well as long as the regularizer is not too close to zero.


For each identified term, we apply a significance test on the relationship between the presence of each term and the demographic indicators shown in the columns of the table.

we apply the Wald test for comparing the means of independent samples, while making the Bonferroni correction for multiple comparisons (Wasserman, 2003).


1 The data sets are available for download at http://cogcomp.cs.illinois.edu/Data A local D2W approach disambiguates each mention m i separately.

let φ(m i , t j ) be a score function reflecting the likelihood that the candidate title t j ∈ W is the correct disambiguation for m i ∈ M .


We represent the functions φ and ψ as weighted sums of features.

we set: where each feature φ i (m, t) captures some aspect of the relatedness between the mention m and the Wikipedia title t. Feature functions ψ i (t, t ′ ) are defined analogously.


The first data set, from (Milne and Witten, 2008b), is a subset of the AQUAINT corpus of newswire text that is annotated to mimic the hyperlink structure in Wikipedia.

only the first mentions of "important" titles were hyperlinked.


In BOT, the set of titles is collected from the mentions hyperlinked in the gold annotation.

if the gold annotation is { (China, People's Republic of China), (Taiwan, Taiwan) Table 3: Percent of "solvable" mentions as a function of the number of generated disambiguation candidates.


This kind of solver sequentially decides whether the local sequence of characters makes up a word as well as its possible POS tag.

a word-based solver reads the input sentence from left to right, predicts whether the current piece of continuous characters is a word token and which class it belongs to.


Here, the word local means the labels of nearby characters are not used as features.

the local character classifier assumes that the tags of characters are independent of each other.


The key point for the intermediate sub-word structures is to maximize the agreement of the three coarse-grained systems.

the goal is to make merged sub-words as large as possible but not overlap with any predicted word produced by the three coarse-grained solvers.


In other words, the goal is to make merged sub-words as large as possible but not overlap with any predicted word produced by the three coarse-grained solvers.

if the position between two continuous characters is predicted as a word boundary by any segmenter, this position is taken as a separation position of the sub-word sequence.


This is a crucial difference with our work.


In testing, unknown words are generated after the generation of symbol UNKNOWN, and we define their probability by a first-order Markov model.

given a flat word w = c 1 c 2 • • • c n not seen in training, we define its probability conditioned with the part-of-speech p as where c 0 is taken to be a START symbol indicating the left boundary of a word and c n+1 is the STOP symbol to indicate the right boundary.


Having said that, it must be pointed out that the comparison is meaningful only in a limited sense, as in previous literatures on Chinese parsing, the input is always word segmented or even part-of-speech tagged.

the bracketing in our case is around characters instead of words.


The first summand of Formula (2) corresponds to accuracy, while the second is adding an arbitrary constant weight of 0.5 to the proportion of unanswered questions.

unanswered questions are receiving the same value as if half of them had been answered correctly.


The sensitivity of the measure is the number of times among all the comparisons in the experiment where this performance difference is obtained (Sakai, 2007b).

the more comparisons accomplish the estimated performance difference, the more sensitive is the measure.


The third baseline uses a graph constructed only with Lin's thesaurus, without using supervised data.

we followed the same scheme as in §4.1 but with the hyperparameter α = 0.


We found that unlike other axioms, this axiom often yield a relation type that is already present in the semantic representation.

it often yields R(x, z) when R(x', z) is already known.


In future work we hope to further improve unsupervised semantic parsing performance.

we intend to explore new approaches for confidence estimation and their usage in the unsupervised and semi-supervised versions of the task.


The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and (b) consumers' opinions on the important aspects greatly influence their overall opinions on the product.

given consumer reviews of a product, we first identify the product aspects by a shallow dependency parser and determine consumers' opinions on these aspects via a sentiment classifier.


We then design an aspect ranking algorithm to identify the important aspects by simultaneously taking into account the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions.

we assume that consumer's overall opinion rating on a product is generated based on a weighted sum of his/her specific opinions on multiple aspects of the product, where the weights essentially measure the degree of importance of the aspects.


While these candidates may contain much noise, we leverage the Pros and Cons reviews to assist identify aspects from the candidates.

we explore the frequent noun terms in Pros and Cons reviews as features, and train a one-class SVM (Manevitz et al., 2002) to identify aspects in the candidates.


While the obtained aspects may contain some synonym terms, such as "earphone" and "headphone," we further perform synonym clustering to get unique aspects.

we first expand each aspect term with its synonym terms obtained from the synonym terms Web site 2 , and then cluster the terms to obtain unique aspects based on 1 http://nlp.stanford.edu/software/lex-parser.shtml 2 http://thesaurus.com unigram feature.


To this end, we here utilize Pros and Cons reviews to train a SVM sentiment classifier.

we collect sentiment terms in the Pros and Cons reviews as features and represent each review into feature vector using Boolean weighting.


Given a free text review, since it may cover various opinions on multiple aspects, we first locate the opinionated expression modifying each aspect, and determine the opinion on the aspect using the learned SVM classifier.

since the opinionated expression on each aspect tends to contain sentiment terms and appear closely to the aspect (Hu and Liu, 2004), we select the expressions which contain sentiment terms and are at the distance of less than 5 from the aspect NP in the parsing tree.


Thus, we assume that the consumer gives the overall opinion rating O r based on the weighted sum of his/her opinion o rk on each aspect a k :  To model the uncertainty of the importance weights ω r in each review, we assume ω r as a sample drawn from a Multivariate Gaussian Distribution, with µ as the mean vector and Σ as the covariance matrix, We further incorporate aspect frequency as a prior knowledge to define the distribution of µ and Σ.

the distribution of µ and Σ is defined based on its Kullback-Leibler (KL) divergence to a prior distribution with a mean vector µ 0 and an identity covariance matrix I in Eq.3.


Next, we utilize Maximum Log-likelihood (ML) to estimate the model parameters given the consumer reviews corpus.

we aim to find an optimalΨ to maximize the probability of observing the overall opinion ratings in the reviews corpus.


On average, our approach significantly outperforms Hu's method and Wu' method in terms of F 1 -measure by over 5.87% and 3.27%, respectively.


These observations motivate us to utilize aspect ranking results to assist classifying the sentiment of review documents.

we randomly sampled 100 reviews of each product as the testing data and used the remaining reviews as the training data.


Here, we give more emphasis on the important aspects and the sentiment terms that modify these aspects.

we set the term-weighting as 1 + φ • ϖ k , where ϖ k is the importance score of the aspect a k , φ is set to 100.


The key novelty is that we recognize that certain probability estimates are hard to improve based on classes.

the best probability estimate for frequent events is often the maximum likelihood estimator and this estimator is hard to improve by using other information sources like classes or word similarity.


We therefore design a model that attempts to focus the effect of class-based generalization on rare events.

we propose to employ the same strategy for this that history-length interpolated (HI) models use.


For a given ordered list of scores, this measure is based on the difference between the original order, and the order when the list is sorted by score.

if items are ranked optimally in descending order of score at position N , nDCG-N is equal to 1. nDCG is a normalised score, and indicates how close the candidate label ranking is to the optimal ranking within the set of annotated candidates, noting that an nDCG-N score of 1 tells us nothing about absolute values of the candidates.


A few pilot studies have investigated the task in recent years and exiting methods make use of either the information in the source language or the information in the target language after using machine translation.

for the task of English-to-Chinese cross-language summarization, one method is to directly extract English summary sentences based on English features extracted from the English documents, and then automatically translate the English summary sentences into Chinese summary sentences.


In this study, we propose to leverage both the information in the source language and the information in the target language for cross-language document summarization.

we propose two graph-based summarization methods (SimFusion and CoRank) for using both Englishside and Chinese-side information in the task of English-to-Chinese cross-document summarization.


The sentence similarities in the two languages are fused in the method.

when we compute the similarity value between two Chinese sentences, the similarity value between the corresponding two English sentences is used by linear fusion.


The second assumption is similar to HITS if the English sentences and the Chinese sentences are considered as authorities and hubs, respectively.

the proposed method aims to fuse the ideas of PageRank and HITS in a unified framework.


This family of similarity functions includes the cosine In contrast to the automatic measures devised in the previous section, we might have access to human annotated data.

use label information such as topic or genre to define the set of similar articles.


The metadata field HL contains headlines, SO source info, and  Topic On the basis of the same meta-data, we devised a classification of the Penn Treebank WSJ by topic.

while the genre division has been mostly made on the basis of headlines, we use the information of the IN field.


The line of investigation we pursue in this paper is to consider more efficient exact algorithms.

we test different variants of the classical A* algorithm (Hart et al., 1968), which has met with success in Penn Treebank parsing with context-free grammars (Klein and Manning, 2003;Pauls and Klein, 2009a;Pauls and Klein, 2009b).


The results show that PCFG actually finds worse results as it considers more of the search space.

the supertagger can actually "fix" a bad parsing model by restricting it to a small portion of the search space.


We use the Columbia Arabic Treebank (CATiB) .

we use the portion converted automatically from part 3 of the Penn Arabic Treebank (PATB) (Maamouri et al., 2004) to the CATiB format, which enriches the CATiB dependency trees with full PATB morphological information.


Fourth and most importantly, for most combinations of parameter settings and on both query sets, the runs that take advantage of query logs (R p , R s , R u ) produce the highest scores.

when I-per-Q is set to 10 and C-per-I to 20, run R u identifies the original query as an exact match among the top three to four class labels returned (score 0.278); and as a partial match among the top one to two class labels returned (score 0.636), as an average over the Q e set.